The microbiome and metabolism

Sumeed Yoyo Manzoor

March 26, 2020

WT vs L-Bmal1-KO data analysis

conclusions

  • From these graphs, energy expenditure seems similar between WT and KO mice
  • RQ appears different at night
  • ANCOVA results:
Variable Time of day p val variable p val body mass
VO2 Light 0.08547 0.08722
VO2 Dark 0.08230 0.44250
RQ Light 0.1151 0.1177
RQ Dark 0.03446 0.68916
  • L-Bmal1-KO mice and WT mice have similar energy expenditure
  • L-Bmal1-KO mice rely more on carbohydrate metabolism during the night

ANCOVA of remaining variables

Variable Time of day p val variable p val body mass
Food intake Light 0.9239 0.2644
Food intake Dark 0.5019877 0.0100102
Water intake Light 0.48527 0.09441
Water intake Dark 0.06415 0.43000
Directional movement (meters) Light 0.1782 0.2694
Directional movement (meters) Dark 0.35913 0.35804
Directional movement (speed) Light 0.3690 0.1369
Directional movement (speed) Dark 0.99260 0.13208
Ambulatory movement (meters) Light 0.2209 0.1691
Ambulatory movement (meters) Dark 0.2810 0.5566
Rearing Light 0.5170 0.6639
Rearing Dark 0.5779254 0.0176512

Relative Cumulative Frequency (RCF): a potential better practice than averages

  • A clear disadvantage to this method of analysis, if we were to stop here, is that we are ignoring a lot of information.
  • In order to run ANCOVA, we are taking averages, which bunches up all the data and blinds the analysis to variation differences in the data.
  • RCF allows us to find a distribution for the data and calculate an \(EC_{50}\), which is more sensitive to minor deviations in data
  • pipeline: RCF -> ANCOVA

Halatchev, I. G., O’Donnell, D., Hibberd, M. C., & Gordon, J. I. (2019). Applying indirect open-circuit calorimetry to study energy expenditure in gnotobiotic mice harboring different human gut microbial communities. Microbiome, 7(1), 158.
Riachi, M., Himms-Hagen, J., & Harper, M.-E. (2004). Percent relative cumulative frequency analysis in indirect calorimetry: application to studies of transgenic mice. Canadian Journal of Physiology and Pharmacology, 82(12), 1075–1083.

RCF results

Variable Time of day p val variable p val body mass
VO2 Total 0.07782 0.28643
VO2 Light 0.08246 0.07816
VO2 Dark 0.11743 0.86615
RQ Total 0.06315 0.25498
RQ Light 0.19976 0.03199
RQ Dark 0.04075 0.57337

future directions

  • While RCF is better than averaging for certain aspects of data, it is not useful for all datatypes
  • RCF still ignores trend differences between data. CLOCKLAB or other rhythmic analysis tools may be well suited for analyzing trend differences between mice.

PCA of light cycle data

LDFA light cycle

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PCA of dark cycle data

LDFA dark cycle

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GF vs SPF dataset analysis

RCF results

Variable Time of day p val variable p val body mass
VO2 Total 0.02746 0.17883
VO2 Light 0.09232 0.67647
VO2 Dark 0.01712 0.10451
RQ Total 0.16704 0.01449
RQ Light 0.2479580 0.0543059
RQ Dark 0.25874 0.01166
  • GF has lower energy expenditure. GF \(EC_{50}=1.74\), SPF \(EC_{50}=1.90\)
  • Body mass was significant for energy use